Various adaptive filter structures have been developed for use in time updated adaptive systems to solve acoustical echo cancellation, channel equalization and other problems; examples of such structures include, for example, transversal, multistage lattice, systolic array, and recursive implementations. Among these, transversal finite-impulse-response (FIR) filters are often used, due to stability considerations, and to their versatility and ease of implementation. Many algorithms have also been developed to adapt these filters, including the least-mean-squares (LMS), recursive least-squares, sequential regression, and least-squares lattice algorithms.
The method of least squares is sometimes used to derive a set of filter coefficients in an adaptive filter. A deficiency of the least squares method is that it sometimes produces a set of filter coefficients whose performance, when used by a filter, is dependent upon the spectral properties of the signal being processed. This may result in an adaptive system where the set of filter coefficients will have a satisfactory performance in a first range of frequencies, and a very unsatisfactory performance in a second range of frequencies.
Consequently, there is a need in the industry for providing a filter adaptation unit suitable for producing a set of filter coefficients that alleviates at least in part the deficiencies of the prior art.